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Activity Number: 257 - SPEED: Longitudinal/Correlated Data
Type: Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 2:45 PM
Sponsor: Biometrics Section
Abstract #332903
Title: Unified Mediation Analysis Approach to Complex Data of Mixed Types via Copula Models
Author(s): Wei Hao* and Peter X.-K. Song
Companies: University of Michigan and University of Michigan
Keywords: mediation analysis; copula models; mixed data

Motivated by pervasive biomedical data, we propose a unified mediation analysis approach to complex data of mixed types, including continuous, categorical, count variables. We invoke copula models to specify joint distributions of outcome variables, mediators and exposure variables of interest in the context of generalized linear models. We develop inference procedures to evaluate casual pathways in both aspects of parameter estimation and hypothesis testing for direct and/or indirect effects of the exposure variable on outcome variables. Our proposed method also enables us to identify important mediators through which exposure variables have indirect effects. We examine necessary model assumptions for the identifiability of casual effects and establish asymptotic properties for the proposed method. We compare the performance of the proposed method with other existing methods using simulation studies. We apply the proposed method to an analysis of a real biomedical dataset.

Authors who are presenting talks have a * after their name.

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